Knowledge-based recursive least squares techniques for heterogeneous clutter suppression
نویسندگان
چکیده
منابع مشابه
Kernel Recursive Least Squares
We present a non-linear kernel-based version of the Recursive Least Squares (RLS) algorithm. Our Kernel-RLS algorithm performs linear regression in the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum meansquared-error regressor. Sparsity (and therefore regularization) of the solution is achieved by an explicit greedy sparsification proces...
متن کاملRecursive Least Squares Estimation
We start with estimation of a constant based on several noisy measurements. Suppose we have a resistor but do not know its resistance. So we measure it several times using a cheap (and noisy) multimeter. How do we come up with a good estimate of the resistance based on these noisy measurements? More formally, suppose x = (x1, x2, . . . , xn) T is a constant but unknown vector, and y = (y1, y2, ...
متن کاملHierarchic Kernel Recursive Least-Squares
We present a new hierarchic kernel based modeling technique for modeling evenly distributed multidimensional datasets that does not rely on input space sparsification. The presented method reorganizes the typical single-layer kernel based model in a hierarchical structure, such that the weights of a kernel model over each dimension are modeled over the adjacent dimension. We show that the impos...
متن کاملApproximate QR-Based Algorithms for Recursive Nonlinear Least Squares Estiamtion
This paper proposes new approximate QR-based algorithms for recursive nonlinear least squares (NLS) estimation. Two QR decomposition-based recursive algorithms are introduced based on the classical Gauss-Newton (GN) and Levenberg-Marquardt (LM) algorithms in nonlinear unconstrained optimization or least squares problems. Instead of using the matrix inversion formula, recursive QR decomposition ...
متن کاملRank based Least-squares Independent Component Analysis
In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IET Radar, Sonar & Navigation
سال: 2007
ISSN: 1751-8784
DOI: 10.1049/iet-rsn:20060006